---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-hi-8-3-5.6e-05-mt5-small-finetuned
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wiki_lingua
      type: wiki_lingua
      config: hi
      split: test
      args: hi
    metrics:
    - name: Rouge1
      type: rouge
      value: 1.3405
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wiki_lingua-hi-8-3-5.6e-05-mt5-small-finetuned

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4454
- Rouge1: 1.3405
- Rouge2: 0.3957
- Rougel: 1.3311
- Rougelsum: 1.3354

## Baseline Result
- Rouge1: 4.18
- Rouge2: 1.31
- Rougel: 4.08
- Rougelsum: 4.07


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.5276        | 1.0   | 841  | 2.5614          | 1.3305 | 0.3186 | 1.3393 | 1.345     |
| 3.0712        | 2.0   | 1682 | 2.4707          | 1.2656 | 0.2856 | 1.2595 | 1.2631    |
| 2.9584        | 3.0   | 2523 | 2.4454          | 1.3405 | 0.3957 | 1.3311 | 1.3354    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2